706 строки
29 KiB
Python
706 строки
29 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""BERT finetuning runner.
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Finetuning the library models for multiple choice on SWAG (Bert).
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"""
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import argparse
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import csv
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import glob
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import logging
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import os
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import random
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import numpy as np
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import torch
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from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
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from torch.utils.data.distributed import DistributedSampler
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from tqdm import tqdm, trange
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from transformers import WEIGHTS_NAME, AdamW, AutoConfig, AutoTokenizer, get_linear_schedule_with_warmup
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from transformers.modeling_auto import AutoModelForMultipleChoice
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try:
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from torch.utils.tensorboard import SummaryWriter
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except ImportError:
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from tensorboardX import SummaryWriter
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logger = logging.getLogger(__name__)
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class SwagExample(object):
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"""A single training/test example for the SWAG dataset."""
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def __init__(self, swag_id, context_sentence, start_ending, ending_0, ending_1, ending_2, ending_3, label=None):
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self.swag_id = swag_id
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self.context_sentence = context_sentence
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self.start_ending = start_ending
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self.endings = [
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ending_0,
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ending_1,
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ending_2,
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ending_3,
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]
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self.label = label
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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attributes = [
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"swag_id: {}".format(self.swag_id),
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"context_sentence: {}".format(self.context_sentence),
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"start_ending: {}".format(self.start_ending),
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"ending_0: {}".format(self.endings[0]),
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"ending_1: {}".format(self.endings[1]),
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"ending_2: {}".format(self.endings[2]),
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"ending_3: {}".format(self.endings[3]),
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]
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if self.label is not None:
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attributes.append("label: {}".format(self.label))
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return ", ".join(attributes)
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class InputFeatures(object):
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def __init__(self, example_id, choices_features, label):
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self.example_id = example_id
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self.choices_features = [
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{"input_ids": input_ids, "input_mask": input_mask, "segment_ids": segment_ids}
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for _, input_ids, input_mask, segment_ids in choices_features
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]
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self.label = label
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def read_swag_examples(input_file, is_training=True):
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with open(input_file, "r", encoding="utf-8") as f:
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lines = list(csv.reader(f))
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if is_training and lines[0][-1] != "label":
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raise ValueError("For training, the input file must contain a label column.")
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examples = [
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SwagExample(
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swag_id=line[2],
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context_sentence=line[4],
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start_ending=line[5], # in the swag dataset, the
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# common beginning of each
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# choice is stored in "sent2".
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ending_0=line[7],
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ending_1=line[8],
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ending_2=line[9],
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ending_3=line[10],
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label=int(line[11]) if is_training else None,
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)
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for line in lines[1:] # we skip the line with the column names
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]
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return examples
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def convert_examples_to_features(examples, tokenizer, max_seq_length, is_training):
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"""Loads a data file into a list of `InputBatch`s."""
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# Swag is a multiple choice task. To perform this task using Bert,
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# we will use the formatting proposed in "Improving Language
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# Understanding by Generative Pre-Training" and suggested by
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# @jacobdevlin-google in this issue
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# https://github.com/google-research/bert/issues/38.
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#
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# Each choice will correspond to a sample on which we run the
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# inference. For a given Swag example, we will create the 4
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# following inputs:
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# - [CLS] context [SEP] choice_1 [SEP]
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# - [CLS] context [SEP] choice_2 [SEP]
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# - [CLS] context [SEP] choice_3 [SEP]
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# - [CLS] context [SEP] choice_4 [SEP]
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# The model will output a single value for each input. To get the
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# final decision of the model, we will run a softmax over these 4
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# outputs.
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features = []
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for example_index, example in tqdm(enumerate(examples)):
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context_tokens = tokenizer.tokenize(example.context_sentence)
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start_ending_tokens = tokenizer.tokenize(example.start_ending)
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choices_features = []
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for ending_index, ending in enumerate(example.endings):
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# We create a copy of the context tokens in order to be
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# able to shrink it according to ending_tokens
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context_tokens_choice = context_tokens[:]
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ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
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# Modifies `context_tokens_choice` and `ending_tokens` in
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# place so that the total length is less than the
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# specified length. Account for [CLS], [SEP], [SEP] with
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# "- 3"
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_truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
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tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
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segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence length.
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padding = [0] * (max_seq_length - len(input_ids))
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input_ids += padding
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input_mask += padding
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segment_ids += padding
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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choices_features.append((tokens, input_ids, input_mask, segment_ids))
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label = example.label
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if example_index < 5:
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logger.info("*** Example ***")
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logger.info("swag_id: {}".format(example.swag_id))
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for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
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logger.info("choice: {}".format(choice_idx))
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logger.info("tokens: {}".format(" ".join(tokens)))
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logger.info("input_ids: {}".format(" ".join(map(str, input_ids))))
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logger.info("input_mask: {}".format(" ".join(map(str, input_mask))))
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logger.info("segment_ids: {}".format(" ".join(map(str, segment_ids))))
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if is_training:
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logger.info("label: {}".format(label))
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features.append(InputFeatures(example_id=example.swag_id, choices_features=choices_features, label=label))
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return features
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def _truncate_seq_pair(tokens_a, tokens_b, max_length):
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"""Truncates a sequence pair in place to the maximum length."""
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# This is a simple heuristic which will always truncate the longer sequence
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# one token at a time. This makes more sense than truncating an equal percent
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# of tokens from each, since if one sequence is very short then each token
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# that's truncated likely contains more information than a longer sequence.
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while True:
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total_length = len(tokens_a) + len(tokens_b)
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if total_length <= max_length:
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break
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if len(tokens_a) > len(tokens_b):
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tokens_a.pop()
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else:
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tokens_b.pop()
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def accuracy(out, labels):
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outputs = np.argmax(out, axis=1)
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return np.sum(outputs == labels)
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def select_field(features, field):
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return [[choice[field] for choice in feature.choices_features] for feature in features]
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def set_seed(args):
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.n_gpu > 0:
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torch.cuda.manual_seed_all(args.seed)
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def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
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if args.local_rank not in [-1, 0]:
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torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
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# Load data features from cache or dataset file
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input_file = args.predict_file if evaluate else args.train_file
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cached_features_file = os.path.join(
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os.path.dirname(input_file),
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"cached_{}_{}_{}".format(
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"dev" if evaluate else "train",
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list(filter(None, args.model_name_or_path.split("/"))).pop(),
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str(args.max_seq_length),
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),
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)
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if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
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logger.info("Loading features from cached file %s", cached_features_file)
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features = torch.load(cached_features_file)
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else:
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logger.info("Creating features from dataset file at %s", input_file)
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examples = read_swag_examples(input_file)
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features = convert_examples_to_features(examples, tokenizer, args.max_seq_length, not evaluate)
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if args.local_rank in [-1, 0]:
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logger.info("Saving features into cached file %s", cached_features_file)
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torch.save(features, cached_features_file)
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if args.local_rank == 0:
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torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
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# Convert to Tensors and build dataset
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all_input_ids = torch.tensor(select_field(features, "input_ids"), dtype=torch.long)
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all_input_mask = torch.tensor(select_field(features, "input_mask"), dtype=torch.long)
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all_segment_ids = torch.tensor(select_field(features, "segment_ids"), dtype=torch.long)
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all_label = torch.tensor([f.label for f in features], dtype=torch.long)
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if evaluate:
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dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
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else:
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dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
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if output_examples:
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return dataset, examples, features
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return dataset
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def train(args, train_dataset, model, tokenizer):
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""" Train the model """
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if args.local_rank in [-1, 0]:
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tb_writer = SummaryWriter()
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args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
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train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
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train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
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if args.max_steps > 0:
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t_total = args.max_steps
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args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
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else:
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t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
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# Prepare optimizer and schedule (linear warmup and decay)
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no_decay = ["bias", "LayerNorm.weight"]
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optimizer_grouped_parameters = [
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{
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"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
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"weight_decay": args.weight_decay,
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},
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{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
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scheduler = get_linear_schedule_with_warmup(
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optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
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)
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if args.fp16:
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try:
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from apex import amp
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except ImportError:
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raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
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model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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# multi-gpu training (should be after apex fp16 initialization)
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if args.n_gpu > 1:
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model = torch.nn.DataParallel(model)
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# Distributed training (should be after apex fp16 initialization)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(
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model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
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)
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# Train!
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_dataset))
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logger.info(" Num Epochs = %d", args.num_train_epochs)
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logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
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logger.info(
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" Total train batch size (w. parallel, distributed & accumulation) = %d",
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args.train_batch_size
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* args.gradient_accumulation_steps
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* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
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)
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logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
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logger.info(" Total optimization steps = %d", t_total)
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global_step = 0
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tr_loss, logging_loss = 0.0, 0.0
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model.zero_grad()
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train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
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set_seed(args) # Added here for reproductibility
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for _ in train_iterator:
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epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
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for step, batch in enumerate(epoch_iterator):
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model.train()
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batch = tuple(t.to(args.device) for t in batch)
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inputs = {
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"input_ids": batch[0],
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"attention_mask": batch[1],
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# 'token_type_ids': None if args.model_type == 'xlm' else batch[2],
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"token_type_ids": batch[2],
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"labels": batch[3],
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}
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# if args.model_type in ['xlnet', 'xlm']:
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# inputs.update({'cls_index': batch[5],
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# 'p_mask': batch[6]})
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outputs = model(**inputs)
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loss = outputs[0] # model outputs are always tuple in transformers (see doc)
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if args.n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
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if args.gradient_accumulation_steps > 1:
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loss = loss / args.gradient_accumulation_steps
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if args.fp16:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
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else:
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
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tr_loss += loss.item()
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if (step + 1) % args.gradient_accumulation_steps == 0:
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optimizer.step()
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scheduler.step() # Update learning rate schedule
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model.zero_grad()
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global_step += 1
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if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
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# Log metrics
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if (
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args.local_rank == -1 and args.evaluate_during_training
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): # Only evaluate when single GPU otherwise metrics may not average well
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results = evaluate(args, model, tokenizer)
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for key, value in results.items():
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tb_writer.add_scalar("eval_{}".format(key), value, global_step)
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tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
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tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
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logging_loss = tr_loss
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if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
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# Save model checkpoint
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output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
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model_to_save = (
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model.module if hasattr(model, "module") else model
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) # Take care of distributed/parallel training
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model_to_save.save_pretrained(output_dir)
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tokenizer.save_vocabulary(output_dir)
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torch.save(args, os.path.join(output_dir, "training_args.bin"))
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logger.info("Saving model checkpoint to %s", output_dir)
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if args.max_steps > 0 and global_step > args.max_steps:
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epoch_iterator.close()
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break
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if args.max_steps > 0 and global_step > args.max_steps:
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train_iterator.close()
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break
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if args.local_rank in [-1, 0]:
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tb_writer.close()
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return global_step, tr_loss / global_step
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def evaluate(args, model, tokenizer, prefix=""):
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dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
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if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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os.makedirs(args.output_dir)
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args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
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# Note that DistributedSampler samples randomly
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eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
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eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
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# Eval!
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logger.info("***** Running evaluation {} *****".format(prefix))
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logger.info(" Num examples = %d", len(dataset))
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logger.info(" Batch size = %d", args.eval_batch_size)
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eval_loss, eval_accuracy = 0, 0
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nb_eval_steps, nb_eval_examples = 0, 0
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for batch in tqdm(eval_dataloader, desc="Evaluating"):
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model.eval()
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batch = tuple(t.to(args.device) for t in batch)
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with torch.no_grad():
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inputs = {
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"input_ids": batch[0],
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"attention_mask": batch[1],
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# 'token_type_ids': None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids
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"token_type_ids": batch[2],
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"labels": batch[3],
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}
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# if args.model_type in ['xlnet', 'xlm']:
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# inputs.update({'cls_index': batch[4],
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# 'p_mask': batch[5]})
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outputs = model(**inputs)
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tmp_eval_loss, logits = outputs[:2]
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eval_loss += tmp_eval_loss.mean().item()
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logits = logits.detach().cpu().numpy()
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label_ids = inputs["labels"].to("cpu").numpy()
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tmp_eval_accuracy = accuracy(logits, label_ids)
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eval_accuracy += tmp_eval_accuracy
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|
nb_eval_steps += 1
|
|
nb_eval_examples += inputs["input_ids"].size(0)
|
|
|
|
eval_loss = eval_loss / nb_eval_steps
|
|
eval_accuracy = eval_accuracy / nb_eval_examples
|
|
result = {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy}
|
|
|
|
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
|
|
with open(output_eval_file, "w") as writer:
|
|
logger.info("***** Eval results *****")
|
|
for key in sorted(result.keys()):
|
|
logger.info("%s = %s", key, str(result[key]))
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
|
|
|
return result
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
# Required parameters
|
|
parser.add_argument(
|
|
"--train_file", default=None, type=str, required=True, help="SWAG csv for training. E.g., train.csv"
|
|
)
|
|
parser.add_argument(
|
|
"--predict_file",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="SWAG csv for predictions. E.g., val.csv or test.csv",
|
|
)
|
|
parser.add_argument(
|
|
"--model_name_or_path",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="Path to pretrained model or model identifier from huggingface.co/models",
|
|
)
|
|
parser.add_argument(
|
|
"--output_dir",
|
|
default=None,
|
|
type=str,
|
|
required=True,
|
|
help="The output directory where the model checkpoints and predictions will be written.",
|
|
)
|
|
|
|
# Other parameters
|
|
parser.add_argument(
|
|
"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
|
|
)
|
|
parser.add_argument(
|
|
"--tokenizer_name",
|
|
default="",
|
|
type=str,
|
|
help="Pretrained tokenizer name or path if not the same as model_name",
|
|
)
|
|
parser.add_argument(
|
|
"--max_seq_length",
|
|
default=384,
|
|
type=int,
|
|
help="The maximum total input sequence length after tokenization. Sequences "
|
|
"longer than this will be truncated, and sequences shorter than this will be padded.",
|
|
)
|
|
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
|
|
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
|
|
parser.add_argument(
|
|
"--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
|
|
)
|
|
|
|
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
|
|
parser.add_argument(
|
|
"--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
|
|
)
|
|
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
|
|
parser.add_argument(
|
|
"--gradient_accumulation_steps",
|
|
type=int,
|
|
default=1,
|
|
help="Number of updates steps to accumulate before performing a backward/update pass.",
|
|
)
|
|
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
|
|
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
parser.add_argument(
|
|
"--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
|
|
)
|
|
parser.add_argument(
|
|
"--max_steps",
|
|
default=-1,
|
|
type=int,
|
|
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
|
|
)
|
|
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
|
|
|
|
parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
|
|
parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
|
|
parser.add_argument(
|
|
"--eval_all_checkpoints",
|
|
action="store_true",
|
|
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
|
|
)
|
|
parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
|
|
parser.add_argument(
|
|
"--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
|
|
)
|
|
parser.add_argument(
|
|
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
|
|
)
|
|
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
|
|
|
|
parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
|
|
parser.add_argument(
|
|
"--fp16",
|
|
action="store_true",
|
|
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
|
|
)
|
|
parser.add_argument(
|
|
"--fp16_opt_level",
|
|
type=str,
|
|
default="O1",
|
|
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
|
|
"See details at https://nvidia.github.io/apex/amp.html",
|
|
)
|
|
parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
|
|
parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
|
|
args = parser.parse_args()
|
|
|
|
if (
|
|
os.path.exists(args.output_dir)
|
|
and os.listdir(args.output_dir)
|
|
and args.do_train
|
|
and not args.overwrite_output_dir
|
|
):
|
|
raise ValueError(
|
|
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
|
|
args.output_dir
|
|
)
|
|
)
|
|
|
|
# Setup distant debugging if needed
|
|
if args.server_ip and args.server_port:
|
|
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
|
|
import ptvsd
|
|
|
|
print("Waiting for debugger attach")
|
|
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
|
|
ptvsd.wait_for_attach()
|
|
|
|
# Setup CUDA, GPU & distributed training
|
|
if args.local_rank == -1 or args.no_cuda:
|
|
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
|
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
|
|
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
|
torch.cuda.set_device(args.local_rank)
|
|
device = torch.device("cuda", args.local_rank)
|
|
torch.distributed.init_process_group(backend="nccl")
|
|
args.n_gpu = 1
|
|
args.device = device
|
|
|
|
# Setup logging
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
|
|
)
|
|
logger.warning(
|
|
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
|
|
args.local_rank,
|
|
device,
|
|
args.n_gpu,
|
|
bool(args.local_rank != -1),
|
|
args.fp16,
|
|
)
|
|
|
|
# Set seed
|
|
set_seed(args)
|
|
|
|
# Load pretrained model and tokenizer
|
|
if args.local_rank not in [-1, 0]:
|
|
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
|
|
|
|
config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,)
|
|
model = AutoModelForMultipleChoice.from_pretrained(
|
|
args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
|
|
)
|
|
|
|
if args.local_rank == 0:
|
|
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
|
|
|
|
model.to(args.device)
|
|
|
|
logger.info("Training/evaluation parameters %s", args)
|
|
|
|
# Training
|
|
if args.do_train:
|
|
train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
|
|
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
|
|
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
|
|
|
|
# Save the trained model and the tokenizer
|
|
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
|
|
logger.info("Saving model checkpoint to %s", args.output_dir)
|
|
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
|
|
# They can then be reloaded using `from_pretrained()`
|
|
model_to_save = (
|
|
model.module if hasattr(model, "module") else model
|
|
) # Take care of distributed/parallel training
|
|
model_to_save.save_pretrained(args.output_dir)
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
|
|
# Good practice: save your training arguments together with the trained model
|
|
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
|
|
|
|
# Load a trained model and vocabulary that you have fine-tuned
|
|
model = AutoModelForMultipleChoice.from_pretrained(args.output_dir)
|
|
tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
|
|
model.to(args.device)
|
|
|
|
# Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
|
|
results = {}
|
|
if args.do_eval and args.local_rank in [-1, 0]:
|
|
if args.do_train:
|
|
checkpoints = [args.output_dir]
|
|
else:
|
|
# if do_train is False and do_eval is true, load model directly from pretrained.
|
|
checkpoints = [args.model_name_or_path]
|
|
|
|
if args.eval_all_checkpoints:
|
|
checkpoints = list(
|
|
os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
|
|
)
|
|
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs
|
|
|
|
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
|
|
|
for checkpoint in checkpoints:
|
|
# Reload the model
|
|
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
|
|
model = AutoModelForMultipleChoice.from_pretrained(checkpoint)
|
|
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
|
model.to(args.device)
|
|
|
|
# Evaluate
|
|
result = evaluate(args, model, tokenizer, prefix=global_step)
|
|
|
|
result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items())
|
|
results.update(result)
|
|
|
|
logger.info("Results: {}".format(results))
|
|
|
|
return results
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|